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Clustering of Words using Dictionary-Learnt Word Representations

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Clustering of Words using Dictionary-Learnt Word Representat...

International Conference on Advances in Computing, Communications and Informatics

作者： Remya R. K. Menon Gargi, S. Samili, S. Department of Computer Science and Applications Amrita School of Engineering Amrita Vishwa Vidyapeetham Amrita University

ISBN: (纸本)9781509020300

Language is the way of communication through words. This will help to get a better insight of the world. Natural Language Processing (NLP) mainly concentrate on expanding systems that allow computers to communicate with people using everyday language. One of the challenges inherent in NLP is teaching computers to recognize the way humans learn and use language. Word representations give rise to capture syntactic and semantic properties of words. So the main purpose of this work is to find out the set of words which have similar semantics by matching the context in which the words occur. In this work we explore a new method for learning word representations using sparse coding, a technique usually done on signals and images. We present an efficient sparse coding algorithm, Orthogonal Matching Pursuit to generate the sparse code. Based on the input given, sparse codes are generated for the input. The input term vectors are classified based on the sparse code by grouping the terms which have same sparse code into one class. K-Means algorithm is also used to classify the input terms vectors which have semantic similarities. Finally, this paper makes a comparison that gives the best representation from the sparse code and K-Means. The result shows an improved set of similar words using sparse code when compared to K-Means. This is because SVD is used as a part of dictionary learning which captures the latent relationship that exists between the words.

关键词： NLP Sparse Coding Dictionary Learning K-Means OMP K-SVD Terms PMI

Harnessing the discriminatory strength of dictionaries

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Harnessing the discriminatory strength of dictionaries

International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Remya R K Menon Neethu V Kini G Athira Krishnan Department of Computer Science and Applications Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri Kollam

ISBN: (纸本)9781509020300

Over the past few years there are many developments in the area of classification in data mining. Classification is a supervised learning method, that maps data into predefined groups or classes. Nowadays classification techniques are extensively used in different applications. In this area most of the research works are done on text, image, signal etc. The main goal of this paper is to use a dictionary-based approach to learn, represent and classify documents. We consider dictionary as a collection of documents and each document in the dictionary is represented as a collection of vectors. An algorithm is also implemented to easily locate a class specific document in the dictionary and if it is not present, update the dictionary. The existing method is based on a dictionary learning algorithm which only improves the document representation based on Singular Value Decomposition (SVD) updation. Since SVD will not be helpful for discrimination of data, so our proposed algorithm is Linear Discriminant Analysis (LDA) for learning a discriminating dictionary. On applying the proposed algorithm on well known dataset, the overall results obtained shows 90% improvement in accuracy. The advantage is that it can be used for both representation as well as classification.

关键词： Dictionaries Algorithm design and analysis Classification algorithms Sparse matrices Matching pursuit algorithms Image coding Signal processing algorithms

A statistical standard siren measurement of the Hubble constant from the LIGO/Virgo gravitational wave compact object merger GW190814 and Dark Energy Survey galaxies

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arXiv 2020年

作者： Palmese, A. Devicente, J. Pereira, M.E.S. Annis, J. Hartley, W. Herner, K. Soares-Santos, M. Crocce, M. Huterer, D. Hernandez, I. Magaña Davis, T.M. Garcia, A. Garcia-Bellido, J. Gschwend, J. Holz, D.E. Kessler, R. Lahav, O. Morgan, R. Nicolaou, C. Conselice, C. Foley, R.J. Gill, M.S.S. Abbott, T.M.C. Aguena, M. Allam, S. Avila, S. Bechtol, K. Bertin, E. Bhargava, S. Brooks, D. Buckley-Geer, E. Burke, D.L. Kind, M. Carrasco Carretero, J. Castander, F.J. Chang, C. Costanzi, M. Costa, L.N. Da Desai, S. Diehl, H.T. Doel, P. Estrada, J. Everett, S. Evrard, A.E. Fernandez, E. Finley, D.A. Flaugher, B. Fosalba, P. Frieman, J. Gaztanaga, E. Gerdes, D.W. Gruen, D. Gruendl, R.A. Gutierrez, G. Hinton, S.R. Hollowood, D.L. Honscheid, K. James, D.J. Kent, S. Krause, E. Kuehn, K. Lin, H. Maia, M.A.G. March, M. Marshall, J.L. Melchior, P. Menanteau, F. Miquel, R. Ogando, R.L.C. Paz-Chinchón, F. Plazas, A.A. Roodman, A. Sako, M. Sanchez, E. Scarpine, V. Schubnell, M. Serrano, S. Sevilla-Noarbe, I. Smith, J. Allyn Smith, M. Suchyta, E. Tarle, G. Troxel, M.A. Tucker, D.L. Walker, A.R. Wester, W. Wilkinson, R.D. Zuntz, J. Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Madrid Spain Department of Physics Brandeis University WalthamMA02453 United States Département de Physique Théorique Center for Astroparticle Physics Université de Genève 24 quai Ernest Ansermet GenevaCH-1211 Switzerland Department of Physics & Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Department of Physics ETH Zurich Wolfgang-Pauli-Strasse 16 ZurichCH-8093 Switzerland Barcelona08034 Spain Campus UAB Carrer de Can Magrans s/n Barcelona08193 Spain Department of Physics University of Michigan Ann ArborMI48109 United States University of Wisconsin-Milwaukee MilwaukeeWI53201 United States School of Mathematics and Physics University of Queensland BrisbaneQLD4072 Australia Instituto de Fisica Teorica UAM/CSIC Universidad Autonoma de Madrid Madrid28049 Spain Laboratório Interinstitucional de e-Astronomia - LIneA Rua Gal. José Cristino 77 Rio de Janeiro RJ20921-400 Brazil Observatório Nacional Rua Gal. José Cristino 77 Rio de Janeiro RJ20921-400 Brazil Department of Astronomy and Astrophysics University of Chicago ChicagoIL60637 United States Physics Department 2320 Chamberlin Hall University of Wisconsin-Madison 1150 University Avenue MadisonWI53706-1390 United States University of Nottingham School of Physics and Astronomy NottinghamNG7 2RD United Kingdom Santa Cruz Institute for Particle Physics Santa CruzCA95064 United States SLAC National Accelerator Laboratory Menlo ParkCA94025 United States Cerro Tololo Inter-American Observatory NSF’s National Optical-Infrared Astronomy Research Laboratory Casilla 603 La Serena Chile Departamento de Física Matemática Instituto de Física Universidade de São Paulo CP 66318 São Paulo SP05314-970 Brazil CNRS UMR 7095 Institut d’Astrophysique de Paris ParisF-75014 F

We present a measurement of the Hubble constant H0 using the gravitational wave (GW) event GW190814, which resulted from the coalescence of a 23 M☉ black hole with a 2.6 M☉ compact object, as a standard siren. No compelling electromagnetic counterpart with associated host galaxy has been identified for this event, thus our analysis accounts for ∼ 2,700 potential host galaxies within a statistical framework. The redshift information is obtained from the photometric redshift (photo-z) catalog from the Dark Energy Survey. The luminosity distance is provided by the gravitational wave sky map published by the LIGO/Virgo Collaboration. Since this GW event has the second–smallest sky localization area after GW170817, GW190814 is likely to provide the best constraint on cosmology from a single standard siren without identifying an electromagnetic counterpart. Our analysis uses photo-z probability distribution functions and corrects for photo-z biases. We also reanalyze the binary–black hole GW170814 within this updated framework. We explore how our findings impact the H0 constraints from GW170817, the only GW merger associated with a unique host galaxy, and therefore the most powerful standard siren to date. From a combination of GW190814, GW170814 and GW170817, our analysis yields H0 = 69.0+−147.5 km s−1 Mpc−1 (68% Highest Density Interval, HDI) for a prior in H0 uniform between [20,140] km s−1 Mpc−1. The addition of GW190814 and GW170814 to GW170817 improves the 68% HDI from GW170817 alone by ∼ 12%, showing how well–localized mergers without counterparts can provide a marginal contribution to standard siren measurements, provided that a complete galaxy catalog is available at the location of the event. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

TMD-Based Phototransistors: Anomalous Broadband Spectrum Photodetection in 2D Rhenium Disulfide Transistor (Advanced Optical Materials 23/2019)

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Advanced Optical Materials 2019年第23期7卷

作者： Du Xiang Tao Liu Junyong Wang Peng Wang Lin Wang Yue Zheng Yanan Wang Jing Gao Kah-Wee Ang Goki Eda Weida Hu Lei Liu Wei Chen Department of Chemistry National University of Singapore 3 Science Drive 3 Singapore 117543 Singapore Centre for Advanced 2D Materials and Graphene Research Centre National University of Singapore 6 Science Drive 2 Singapore 117546 Singapore Department of Physics National University of Singapore 2 Science Drive 3 Singapore 117542 Singapore State Key Laboratory for Infrared Physics Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai 200083 China Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117583 Singapore State Key Laboratory of Luminescence and Applications Changchun Institute of Optics Chinese Academy of Sciences No. 3888 Dongnanhu Road Changchun 130033 P. R. China National University of Singapore (Suzhou) Research Institute 377 Lin Quan Street Suzhou Industrial Park Suzhou Jiangsu 215123 China Joint School of National University of Singapore and Tianjin University International Campus of Tianjin University Binhai New City Fuzhou 350207 China

Information vs. Uncertainty as the foundation for a science of environmental modeling

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arXiv 2017年

作者： Nearing, Grey S. Gupta, Hoshin V. NASA Goddard Space Flight Center Hydrological Sciences Laboratory GreenbeltMD United States National Center for Atmospheric Research Research Applications Laboratory BoulderCO United States University of Maryland Baltimore County Department of Computer Science and Electrical Engineering CatonsvilleMD United States University of Arizona Department of Hydrology and Atmospheric Sciences TucsonAZ United States

Information accounting provides a better foundation for hypothesis testing than does uncertainty quantification. A quantitative account of science is derived under this perspective that alleviates the need for epistemic bridge principles, solves the problem of ad hoc falsification criteria, and deals with verisimilitude by facilitating a general approach to process-level diagnostics. Our argument is that the well-known inconsistencies of both Bayesian and classical statistical hypothesis tests are due to the fact that probability theory is an insufficient logic of science. Information theory, as an extension of probability theory, is required to provide a complete logic on which to base quantitative theories of empirical learning. The organizing question in this case becomes not whether our theories or models are more or less true, or about how much uncertainty is associated with a particular model, but instead whether there is any information available from experimental data that might allow us to improve the model. This becomes a formal hypothesis test, provides a theory of model diagnostics, and suggests a new approach to building dynamical systems models. Copyright © 2017, The Authors. All rights reserved.

关键词： Information theory

THE n-QUEENS PROBLEM: A NEW APPROACH

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arXiv 2017年

作者： Bača, M. López, S.C. Muntaner-Batle, F.A. Semaničová-Feňovčíková, A. Department of Applied Mathematics and Informatics Technical University in Koice Letná 9 Košice Slovakia Departament de Matemàtica Universitat de Lleida C/Jaume II 69 Lleida25001 Spain Graph Theory and Applications Research Group School of Electrical Engineering and Computer Science Faculty of Engineering and Built Environment University of Newcastle NSW2308 Australia Department of Applied Mathematics and Informatics Technical University in Košice Letná 9 Košice Slovakia

Let D be a digraph, possibly with loops. A queen labeling of D is a bijective function l: V (G) −→ {1, 2, . . ., |V (G)|} such that, for every pair of arcs in E(D), namely (u, v) and (u′, v′) we have (i) l(u) + l(v) 6= l(u′) + l(v′) and (ii) l(v) − l(u) 6= l(v′) − l(u′). Similarly, if the two conditions are satisfied modulo n = |V (G)|, we define a modular queen labeling. There is a bijection between (modular) queen labelings of 1-regular digraphs and the solutions of the (modular) n-queens problem. The ⊗h-product was introduced in 2008 as a generalization of the Kronecker product and since then, many relations among labelings have been established using the ⊗h-product and some particular families of graphs. In this paper, we study some families of 1-regular digraphs that admit (modular) queen labelings and present a new construction concerning to the (modular) n-queens problem in terms of the ⊗h-product, which in some sense complements a previous result due to Pó*** Codes 05C78 (Primary), 05C76 05C78, 05C76 (Secondary) Copyright © 2017, The Authors. All rights reserved.

关键词： Directed graphs

Modeling the homophily effect between links and communities for overlapping community detection 25

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Modeling the homophily effect between links and communities ...

25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Zhang, Hongyi Zhao, Tong King, Irwin Lyu, Michael R. Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications Shenzhen Research Institute Chinese University of Hong Kong Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong

Overlapping community detection has drawn much attention recently since it allows nodes in a network to have multiple community memberships. A standard framework to deal with overlapping community detection is Matrix Factorization (MF). Although all existing MF-based approaches use links as input to identify communities, the relationship between links and communities is still under-investigated. Most of the approaches only view links as consequences of communities (community-to-link) but fail to explore how nodes' community memberships can be represented by their linked neighbors (link-to-community). In this paper, we propose a Homophily-based Nonnegative Matrix Factorization (HNMF) to model both-sided relationships between links and communities. From the community-to-link perspective, we apply a preference-based pairwise function by assuming that nodes with common communities have a higher probability to build links than those without common communities. From the link-to-community perspective, we propose a new community representation learning with network embedding by assuming that linked nodes have similar community representations. We conduct experiments on several real-world networks and the results show that our HNMF model is able to find communities with better quality compared with state-of-the-art baselines.

关键词： Matrix factorization

Front Cover: Impact of Nickel Silicide Rear Metallization on the Series Resistance of Crystalline Silicon Solar Cells (Energy Technol. 9/2018)

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Energy Technology 2018年第9期6卷

作者： Rabab R. Bahabry Amir N. Hanna Arwa T. Kutbee Abdurrahman Gumus Muhammad. M. Hussain Integrated Nanotechnology Laboratory and Integrated Disruptive Electronic Applications (IDEA) Laboratory King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Department of Physics University of Jeddah Dhahban 23881-285 Saudi Arabia Department of Physics King Abdulaziz University (KAU) Jeddah 21589-80200 Saudi Arabia Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia

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arXiv 2017年

作者： Surya Prasath, V.B. Abu Alfeilat, Haneen Arafat Lasassmeh, Omar Hassanat, Ahmad B.A. Tarawneh, Ahmad S. Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center OH45229 United States Department of Pediatrics University of Cincinnati College of Medicine CincinnatiOH United States Department of Biomedical Informatics College of Medicine University of Cincinnati OH45267 United States Department of Electrical Engineering and Computer Science University of Cincinnati OH45221 United States Department of Information Technology Mutah University Karak Jordan Department of Algorithm and Their Applications Etvs Lornd University Budapest Hungary

The K-nearest neighbor (KNN) classifier is one of the simplest and most common classifiers, yet its performance competes with the most complex classifiers in the literature. The core of this classifier depends mainly on measuring the distance or similarity between the tested examples and the training examples. This raises a major question about which distance measures to be used for the KNN classifier among a large number of distance and similarity measures available? This review attempts to answer this question through evaluating the performance (measured by accuracy, precision and recall) of the KNN using a large number of distance measures, tested on a number of real-world datasets, with and without adding different levels of noise. The experimental results show that the performance of KNN classifier depends significantly on the distance used, and the results showed large gaps between the performances of different distances. We found that a recently proposed non-convex distance performed the best when applied on most datasets comparing to the other tested distances. In addition, the performance of the KNN with this top performing distance degraded only about 20% while the noise level reaches 90%, this is true for most of the distances used as well. This means that the KNN classifier using any of the top 10 distances tolerate noise to a certain degree. Moreover, the results show that some distances are less affected by the added noise comparing to other distances. Copyright © 2017, The Authors. All rights reserved.

关键词： Machine learning